Industrial controllers are special-purpose computers utilized for controlling industrial processes, manufacturing equipment, and other factory automation, such as data collection or networked systems. One type of industrial controller at the core of an industrial control system is a logic processor such as a programmable logic controller (PLC) or personal computer (PC) based controller. Programmable logic controllers for instance, are programmed by systems designers to operate manufacturing processes via user-designed logic programs or user programs. The user programs are stored in memory and generally executed by the PLC in a sequential manner although instruction jumping, looping and interrupt routines, for example, are also common. Associated with the user program are a plurality of memory elements or variables that provide dynamics to PLC operations and programs.
Connected to the PLC are input/output (I/O) devices. I/O devices provide connection to the PLC for both automated data collection devices such as limit switches, photoeyes, load cells, thermocouples, etc. and manual data collection devices such as keypads, keyboards, pushbuttons, etc. Differences in PLCs are typically dependent on number of I/O they can process, amount of memory, number and type instructions and speed of the PLC central processing unit (CPU).
Another type of industrial controller at the core of an industrial control system is the process controller of a distributed control system (DCS). The process controller is typically programmed by a control engineer for continuous process control such as an oil refinery or a bulk chemical manufacturing plant. A control engineer typically configures control elements such as proportional-integral-derivative (PID) control loops to continuously sample the I/O data, known as the process variable, from the process, compare the process variable to a configured set point and output an error signal, proportional to the difference between the set point and the process variable, to the control device. The control device then adjusts the element controlling the process property, such as a valve in a pipe for flow control or a heating element in a distillation column for temperature control, in an attempt to minimize the error signal. As the DCS name implies, many process controllers are distributed around the process and are communicatively coupled to each other forming the overall control system.
Connected to the process controller are similar types of I/O devices as connected to the PLC and additionally, intelligent I/O devices more common to the process control industry. These intelligent devices have embedded processors capable of performing further calculations or linearization of the I/O data before transmission to the process controller.
A visualization system is generally connected to the industrial controller to provide a human-friendly view into the process instrumented for monitoring or control. The user of a visualization system configures one or more graphical displays representing some aspect of the process the industrial controller is controlling or monitoring. The graphical displays each contain a user configured number of data values collected from the I/O connected to the industrial controller and considered by the user as relevant to the particular graphical display or process area of interest. Other data points may be configured strictly for archival purposes or to generate reports related to interests such as production, downtime, operator efficiency, raw material usage, etc.
Automating control and human-machine interface (HMI) to achieve efficiency, quality, safety, performance, etc. generally requires expertise to be embodied in a model. Relationships between certain data parameters are determined in order to prompt an operator or to facilitate automatic control events. Often such models benefit from tendencies for setting up identical processing lines (e.g., batch, continuous, or discrete). Thus, the investment in such models can be realized across sites of an enterprise.
However, generally such models, if in existence at all, tend to be an incomplete solution. Production lines can be customized for particular applications. Substituted hardware can perform similarly, but not identically, to that which was modeled. Ambient parameters (e.g., humidity, temperature, etc.) can vary from location to location and from time to time within a facility. Materials input into a process can vary from batch to batch or from different suppliers. Further, relationships between certain parameters can go unappreciated during the development of a process model. Although increasingly data is available from ubiquitous integrated devices and sensors, the large amount of data does not necessarily enhance understanding of why an industrial process is departing from a desired optimum condition.
Compensating to an extent for the limitations in automated sensing and control, operators can become exceedingly skilled in detecting aberrations in an industrial process, perhaps accessing data from the HMI in an unexpected way in order to locate indications of where the problem originates. Other operators, though, such as with less experience and on another shift, can lack the intuition in order to identify such solutions.